Search Results (438)

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Accurate weather classification plays a vital role in disaster management and minimizing economic losses. However, satellite-based weather classification remains challenging due to high inter-class similarity; the computational complexity of existing deep learning models, which limits real-time deployment on resource-constrained edge devices; and the limited interpretability of model decisions in practical environments. To address these challenges, this study proposes SatNet-B3, a quantized, lightweight deep learning framework that integrates an EfficientNetB3 backbone with custom classification layers to enable accurate and edge-deployable weather event recognition from satellite imagery. SatNet-B3 is evaluated on the LSCIDMR dataset and demonstrates high-precision performance, achieving 98.20% accuracy and surpassing existing benchmarks. Ten CNN models, including SatNet-B3, were experimented with to classify eight weather conditions, Tropical Cyclone, Extratropical Cyclone, Snow, Low Water Cloud, High Ice Cloud, Vegetation, Desert, and Ocean, with SatNet-B3 yielding the best results. The model addresses class imbalance and inter-class similarity through extensive preprocessing and augmentation, and the pipeline supports the efficient handling of high-resolution geospatial imagery. Post-training quantization reduced the model size by 90.98% while retaining accuracy, and deployment on a Raspberry Pi 4 achieved a 0.3 s inference time. Integrating explainable AI tools such as LIME and CAM enhances interpretability for intelligent climate monitoring. Full article

Greenland, home to the largest ice sheet in the Northern Hemisphere, provides a crucial digital elevation model (DEM) for understanding polar climate evolution and valuable data for global climate change research. Based on ICESat-2 laser altimetry data collected from satellite observations over Greenland between November 2020 and November 2021, the Shandong University of Science and Technology 2021 DEM (SDUST2021DEM) with 500 m grid resolution at the epoch of May 2021 was constructed using a spatiotemporally fitted subgrid least squares method. The precision of the DEM was evaluated by comparison with National Aeronautics and Space Administration IceBridge data and supplemented by GNSS station measurements. The median difference between the DEM and IceBridge data was −0.33 m, the mean deviation −0.58 m, and the median absolute deviation 2.31 m. The accuracy of SDUST2021DEM exhibits a clear spatial pattern: it is higher in the central ice sheet than at the margins, decreases in regions with complex terrain, and remains more reliable in areas characterized by gentle slopes and flat terrain. Overall, the SDUST2021DEM demonstrates stable accuracy and can reliably produce high-precision DEMs for a specific temporal epoch. Full article

The causes of Arctic surface air temperature rise and the corresponding sea ice decline in the early 20th century are still a matter of debate. One hypothesis, considering the major contribution of the internal variability to the early warming event, is the leading one. This study aims to assess the contributions of the Northern Hemisphere’s leading natural variability modes to winter temperature changes in the Arctic during 20th century. Two methodologies were compared to remove externally forced signals from Arctic SAT observations—linear detrending and subtracting the multi-model ensemble mean, thereby isolating internal variability. The study introduces a novel perspective on regional evaluation across four equal-area Arctic sectors (European, Asian, Pacific, and North Atlantic), uncovering a heterogeneous spatial pattern of the Arctic SAT modulation by climate indices. Statistical analysis reveals northern extratropical modes explain 66% (median) of total variance, with dominance of AMO index in HadCRUT5 detrended observations and only 30% with PDO index prominent in observations-CMIP6 residuals. It is revealed that forced-signal removal data outperforms the detrending procedure in isolating unforced internal dynamics. AMO’s susceptibility to external forcings like greenhouse gases/aerosols is also underscored by the results of the study. Future directions advocate dynamic approaches like large initial-condition ensembles prescribing sea surface temperature/sea ice or isolating modes for causal attribution beyond statistical links. Full article

Serratia species are opportunistic human pathogens found in diverse environmental habitats. Here, we report the first isolation of Serratia nevei from food samples in Nigeria. During a two-month epidemiological surveillance at a local food market in Dutsin-Ma, Katsina State, Nigeria, a total of 180 food samples were collected, and isolation and species identification were performed using chromogenic agar and MicroScan autoSCAN-4, respectively. Antimicrobial susceptibility and minimum inhibitory concentrations (MICs) were determined using the MicroScan autoSCAN-4 system. Strain F129B, recovered from a fresh, unprocessed beef sample, was initially identified as Klebsiella pneumoniae by chromogenic agar and MicroScan autoSCAN-4, and subsequently as Serratia marcescens by MALDI-TOF MS. Only Whole Genome Sequencing (WGS) and bioinformatics analyses confirmed its identity as S. nevei. The strain was then selected for further characterization by Whole Genome Sequencing (WGS) and bioinformatics analyses to confirm its identity. The strain was phenotypically resistant to amoxicillin/clavulanic acid and colistin, with elevated MICs for aztreonam (4 mg/L) and cefuroxime (16 mg/L). In silico analyses of its genome confirmed the isolate as S. nevei, harboring genes conferring resistance to β-lactams (blaSTR-2), aminoglycosides (aac (6′)-Ic), fosfomycin (fosA), streptomycin (satA), and tetracycline (tet (41)). Its virulence repertoire comprises genes associated with adhesion (yidE, yidR, yidQ), colicin tolerance (creA and creD), and heavy metal resistance (czcD, chrBACF operon). These findings underscore the need for genomic characterization for accurate species identification within the Serratia genus. Our findings revealed the emergence of S. nevei in the food supply chain and highlighted its potential for zoonotic transmission. Robust surveillance of the local food supply chain is urgently needed in north-western Nigeria. Full article

Sand dune movements represent a critical global environment challenge. While previous studies have mainly focused on horizontal deformation, this study applies the bistatic InSAR technique to reconstruct high-precision digital elevation models (DEMs) of the desert terrain, enabling quantitative assessment of the height change in sand dunes by the DEM differential method. Although InSAR has been widely applied to monitor the surface deformation over the urban, mining, and landslide areas, its application in the desert area is still rare. In this study, the northwestern Kubuqi desert, where sand dunes are clearly distributed, was selected as the study area. Using the TanDEM-X bistatic InSAR data acquired on 26 December 2012 and 25 January 2018, we generated high-resolution DEMs with an estimated accuracy of RMSE ≈ 0.9 m in non-dune areas, as validated against ICESat-2 reference data. The high-precision DEM is attributed to the application of a parameterized modeling method, which also facilitates the effective implementation of the DEM differential method. Then, the t-test (i.e., a statistical hypothesis method) was used to estimate a minimum detectable height change (i.e., LoD) of approximately ±0.50 m and confirm the significance of observed elevation changes. Based on this, this reveals a net mean dune height decrease of 1.04 m during the study period. In addition, quantitative investigations on the vegetation coverage and the wind conditions provided further evidence supporting the observed reduction in dune height, suggesting that vegetation stabilization has likely inhibited sediment transport. This study demonstrates the potential of bistatic InSAR for monitoring desert geomorphological processes and provides scientific support for designing effective desertification control strategies. Full article

Lake water level variation, reflecting the dynamic balance between water input and loss, is a crucial indicator of climate change and regional hydrological cycles. This is particularly significant in arid Central Asia, where lakes are vital surface water resources and key to ecosystem stability. This study systematically reconstructed water level changes of Lake Zaysan and Lake Ulungur from 2003 to 2024 using high-precision altimetry data from ICESat, CryoSat-2, and ICESat-2 satellites. Results indicate that Lake Zaysan experienced significant water level fluctuations of 5.01 m (21.01 Gt water mass change, where 1 Gt = 10⁹ metric tons) in 2010, 5.12 m (21.47 Gt) in 2013, and 3.53 m (14.80 Gt) in 2024. Lake Ulungur also exhibited notable water level changes during the same period. Our study reveals that water level variations in both lakes are primarily controlled by runoff processes. A highly significant positive correlation exists between lake level anomalies and discharge anomalies. Conversely, the low correlation between water levels and precipitation indicates a pronounced lagged effect of snowfall, as lake water level fluctuations are driven by a combination of spring snowmelt runoff and summer precipitation. Furthermore, these findings highlight the sensitive response of these Central Asian lakes to environmental changes under climate warming. Our study enriches observational data on regional lake dynamics and provides a scientific basis for water resource management and future climate adaptation strategies in arid regions. Full article

Channel geometry, e.g., riverbed elevation and channel width, is the fundamental input for hydrodynamic simulations and conveys critical information for understanding fluvial processes. In remote or data-scarce areas, however, traditional field surveys face financial and technical challenges for providing enough spatiotemporal coverage. This study proposes an innovative method integrating multi-source satellite data (Sentinel-2 and ICESat-2) and hydraulic modeling to derive channel geometry for part of the Nen River, China. Both channel width (R² = 0.98, RMSE = 35.41 m) and bottom elevation (R² = 0.86, RMSE = 1.77 m, PBIAS = −0.61%) are well predicted. The satellite-derived channel geometry results in an overall good simulation of 1D flows through the 5-yr period in terms of peak magnitudes and timings, with the NSE value of 0.94, RMSE of 207.76 m³/s, and PBIAS of 6.19%. The 2D inundation driven by the derived channel geometry achieved accurate hydrodynamic responses. However, for the channel bend with complicated flow regimes, the satellite-derived channel terrains tend to generate more different flow rates due to the hypothesized rectangular channel. This proposed method provides a promising way to derive river bathymetry in both low-gradient and high-slope regions where precise river topography is difficult to obtain. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an $RMSE$ of 5.52 m, and an $MAE$ of 4.08 m. The XGBoost model obtained $r$ = 0.716, $RMSE$ = 5.55 m, and $MAE$ = 4.10 m using the same data combination as the Random Forest model, which produced $r$ = 0.706, $RMSE$ = 5.63 m, and $MAE$ = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage. Full article

Accurate, scalable mapping of coral reef habitats is essential for monitoring ecosystem health and detecting change over time. In this study, we introduce a novel mathematically based nonlinear spectral unmixing method for benthic habitat classification, which provides sub-pixel estimates of benthic composition, capturing the mixed benthic composition within individual pixels. We compare its performance against two machine learning approaches: semi-supervised K-Means clustering and AdaBoost decision trees. All models were applied to high-resolution PlanetScope satellite imagery and ICESat-2-derived terrain metrics. Models were trained using a ground truth dataset constructed from benthic photoquadrats collected at Heron Reef, Australia, with additional input features including band ratios, standardized band differences, and derived ICESat-2 metrics such as rugosity and slope. While AdaBoost achieved the highest overall accuracy ($93.3\%$) and benefited most from ICESat-2 features, K-Means performed less well ($85.9\%$) and declined when these metrics were included. The spectral unmixing method uniquely captured sub-pixel habitat abundance, offering a more nuanced and ecologically realistic view of reef composition despite lower discrete classification accuracy ($64.8\%$). These findings highlight nonlinear spectral unmixing as a promising approach for fine-scale, transferable coral reef habitat mapping, especially in complex or heterogeneous reef environments. Full article

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article

The validation of altimeter data in the coastal zones is of great importance for monitoring coastal sea level changes. Therefore, this study focuses on the validation of sea level anomaly (SLA) estimates from three altimetry missions (i.e., SWOT, ICESat-2 and Sentinel-3A) within the distance band of 50 km to the coast in two study areas: the coastal region of East Asia (0° N–40° N, 100° E–140° E) and the US West Coast (30° N–60° N, 145° W–115° W). The selection of these three missions is because they carry the advanced radar and laser altimeters. Although the validation of any single altimeter is not new, the comparison of their performance together in the coastal zones is the first time to our knowledge. Because the spatial resolutions of these three altimeters are different, the spatially averaged altimeter measurements are used for the validation against tide gauges. Moreover, the validation is conducted over four coastal strips (0–5 km, 5–10 km, 10–20 km, and 20–50 km) to better reveal their performance when approaching towards the coastlines. The results show that these three missions achieve similar performance in terms of correlation coefficient and Root Mean Square Error (RMSE) in the 5–50 km coastal strip. The superior performance of the SWOT mission to the ICESat-2 and Sentinel-3A is observed in the last 5 km to coasts (0.06 m/0.73 against 0.09 m/0.70 and 0.12 m/0.63 for coastal regions of East Asia, 0.11 m/0.79 against 0.10 m/0.82 and 0.14 m/0.72 for the US West Coast), where the land contamination is the most significant. The ICESat-2 achieves the best performance (0.10 m) in the US West Coast due to the reduced range bias in higher latitudes, and the SWOT outperforms in the lower-latitude East Asia coastal region (0.06 m). To further investigate the data quality of the SWOT mission, a triple collocation model is applied to quantify the errors. The results reveal that the SWOT obtains similar error variance relative to the tide gauges in both study areas (i.e., 0.010 m² vs. 0.005 m² for the coastal region of East Asia, and 0.010 m² vs. 0.007 m² for the US West Coast). The above findings highlight the SWOT’s advantages in monitoring the coastal sea level changes. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

In the Inner Mongolia Plateau Lake Zone (IMP), situated in China’s semi-arid region, its lake water storage change plays a critical role in wetland ecosystem conservation and regional water security through its lake water storage dynamics. To investigate long-term lake water storage (LWS) changes, this study proposes a novel lake monitoring framework that reconstructs historical lake level time series and estimates water level variations in lakes without altimetry data. Using multi-source satellite data, we quantified LWS variations (2000–2021) across 109 lakes (≥5 km²) on the IMP and examined their spatiotemporal patterns. Our results reveal a net decline of 1.21 Gt in total LWS over the past two decades, averaging 0.06 Gt/yr. A distinct shift occurred around 2012: LWS decreased by 10.82 Gt from 2000 to 2012 but increased by 9.61 Gt from 2013 to 2021. Spatially, significant LWS reductions were concentrated in the central and eastern IMP, resulting from intensive water diversion and groundwater exploitation. In contrast, increases were observed mainly in the western and southern regions, driven by enhanced precipitation and reduced aridity. The findings improve understanding of lake dynamics in semi-arid China over the last two decades and offer technical guidance for sustainable water resource management. Full article

Cloud Top Height (CTH) is a fundamental parameter in atmospheric science, critically influencing Earth’s radiation budget and hydrological cycle. Satellite-based passive remote sensing provides the primary means of monitoring CTH on a global scale due to its extensive spatial coverage. However, these passive retrieval techniques often rely on idealized physical assumptions, leading to significant systematic biases. To quantify these biases, this study provides an evaluation of two prominent passive CTH products, i.e., Sentinel-5P (S5P, O₂ A-band) and Suomi-NPP (NPP, thermal infrared), by comparing their global data from July 2018 to June 2019 against the active CloudSat/CALIPSO (CC) reference. The results reveal stark and complementary error patterns. For single-layer liquid clouds over land, the products exhibit opposing biases, with S5P underestimating CTH while NPP overestimates it. For ice clouds, both products show a general underestimation, but NPP is more accurate. In challenging two-layer scenes, both retrieval methods show large systematic biases, with S5P often erroneously detecting the lower cloud layer. These distinct error characteristics highlight the fundamental limitations of single-sensor retrievals and reveal the potential to organically combine the advantages of different products to improve CTH accuracy. Full article